TY  - CONF
A1  - Soomro, M.H.
A1  - Badruddin, N.
A1  - Yusoff, M.Z.
A1  - Malik, A.S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881076194&doi=10.1109%2fCSPA.2013.6530028&partnerID=40&md5=4a14bdcb47e7e4b42f9dfdaabf375ad3
EP  - 134
Y1  - 2013///
SN  - 9781467356091
N2  - The electroencephalography (EEG) recordings are mostly contaminated by eye blink artifacts. It is very difficult to analyze and interpret the EEG signal due to frequent occurrence of the eye blink artifact. In this paper, a new hybrid algorithm that automatically removes the eye blink artifact from the EEG, based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is proposed. The proposed algorithm is evaluated on simulated EEG to calculate correlation coefficient and signal-to-artifact ratio (SAR). A non-corrected EEG was simulated to have a SAR of -19.1673 dB. From the simulation results, the highest average correlation coefficient and SAR of corrected EEG from non-corrected EEG are obtained as 0.871094 and 2.71645 dB respectively by applying proposed algorithm. The results demonstrate that proposed method recovers the EEG data by removing the eye blink artifacts reliably. In addition, the proposed method is applied on real spontaneous EEG data with eye blink artifact. © 2013 IEEE.
N1  - cited By 26; Conference of 2013 IEEE 9th International Colloquium on Signal Processing and its Applications, CSPA 2013 ; Conference Date: 8 March 2013 Through 10 March 2013; Conference Code:98042
SP  - 129
TI  - A method for automatic removal of eye blink artifacts from EEG based on EMD-ICA
ID  - scholars3525
KW  - Correlation coefficient; Empirical Mode Decomposition; Eye-blink artifacts; Fast-ICA; Independent component analysis(ICA); Signal-to-artifact ratio (SAR)
KW  -  Adaptive filtering; Algorithms; Electroencephalography; Electrophysiology; Independent component analysis
KW  -  Signal processing
CY  - Kuala Lumpur
AV  - none
ER  -